#!/usr/bin/bash




trainset=/home/gaoxinglong/env/tools/speech_tools/kaldi/egs/librispeech/us48_s5/exp/tri5b_ali_a_c/ali2pdfs/chunk_feats_labels
#trainset=/home/gaoxinglong/env/tools/speech_tools/kaldi/egs/librispeech/us48_s5/exp/tri5b_ali_train_2kshort85/ali2pdfs/chunk_feats_labels
#trainset=/home/gaoxinglong/env/tools/speech_tools/kaldi/egs/librispeech/us48_s5/exp/tri5b_ali_180w-score8585/ali2pdfs/chunk_feats_labels
feats=$trainset/final_feats_scps.list
labels=$trainset/final_labels_scps.list


batch_size=64
start_lr=0.0002
final_lr=0.00002
num_labels=5208
#num_labels=6600
epochs=10
devices="0,1,2,3"
model_dir=states_model

python train-state-on-frames.py --gpu_divices=$devices --epoch=$epochs \
--feats_scp_list=$feats --labels_scp_list=$labels --save_dir=$model_dir \
--batch_size=$batch_size --output_num=$num_labels --start_lr=$start_lr --final_lr=$final_lr --restart_model=states_model/epoch_5_iter_500_model.pt

echo 'training done.'
